Abstract
Background:
New clinically important postoperative atrial fibrillation (POAF) is the most common arrhythmia after thoracoscopic anatomical lung cancer surgery and is associated with increased morbidity and mortality. The full spectrum of predictors remains unclear, and effective assessment tools are lacking. This study aimed to develop and externally validate a novel model for predicting new clinically important POAF.
Methods:
This retrospective study included 14 074 consecutive patients who received thoracoscopic anatomical lung cancer surgery from January 2016 to December 2018 in Shanghai Chest Hospital. Based on the split date of 1 January 2018, we selected 8717 participants for the training cohort and 5357 participants for the testing cohort. For external validation, we pooled 2941 consecutive patients who received this surgical treatment from July 2016 to July 2021 in Shanghai Ruijin Hospital. Independent predictors were used to develop a model and internally validated using a bootstrap-resampling approach. The area under the receiver operating characteristic curves (AUROCs) and Brier score were performed to assess the model discrimination and calibration. The decision curve analysis (DCA) was used to evaluate clinical validity and net benefit. New clinically important POAF was defined as a new-onset of POAF that causes symptoms or requires treatment.
Results:
Multivariate analysis suggested that age, hypertension, preoperative treatment, clinical tumor stage, intraoperative arrhythmia and transfusion, and operative time were independent predictors of new clinically important POAF. These seven candidate predictors were used to develop a nomogram, which showed a concordance statistic (C-statistic) value of 0.740 and good calibration (Brier score; 0.025). Internal validation revealed similarly good discrimination (C-statistic, 0.736; 95% CI: 0.705–0.768) and calibration. The decision curve analysis showed positive net benefits with the threshold risk range of 0–100%. C-statistic value and Brier score were 0.717 and 0.028 in the testing cohort, and 0.768 and 0.012 in the external validation cohort, respectively.
Conclusions:
This study identified seven predictors of new clinically important POAF, among which preoperative treatment, intraoperative arrhythmia, and operative time were rarely reported. The established and externally validated model has good performance and clinical usefulness, which may promote the application of prevention and treatment in high-risk patients, and reduce the development and related adverse outcomes of this event.
Keywords: lung cancer, nomogram, postoperative atrial fibrillation, thoracoscopic surgery
Introduction
Highlights
This multicenter retrospective study identified seven predictors of new clinically important postoperative atrial fibrillation, among which preoperative treatment, intraoperative arrhythmia, and operative time were rarely reported.
Using these seven predictors, this study developed a novel model to predict the risk of new clinically important postoperative atrial fibrillation, which showed good performance both in the testing and external validation cohorts.
The decision curve analysis showed the expected net benefit for each patient and intervention decisions based on the predictive model were clearly beneficial when the threshold risk range of 0–100%.
The utilization of this proposed model may promote the application of prevention and treatment in high-risk patients, and reduce the development and related adverse outcomes of this event.
Thoracoscopic surgery, as a minimally invasive surgical approach, is increasingly used for lung resection, which can greatly reduce surgical stress, systemic inflammation, and postoperative complications1–3. Combined with systemic lymph node dissection, it has become the optimal treatment for resectable lung cancer4. New clinically important postoperative atrial fibrillation (POAF) is the most common arrhythmia in patients undergoing thoracoscopic anatomical lung cancer surgery, with a reported incidence of 2.5–20%5–9, and is associated with an increased risk of short-term and long-term adverse outcomes6–8,10.
Many attempts have been made to identify the predictors of new clinically important POAF. Previous studies have identified age, male sex, race, hypertension, diabetes mellitus, history of arrhythmias and congestive heart failure, elevated preoperative resting heart rate, advanced tumor stage, brain natriuretic peptide (BNP) levels, extensive lung resection, and intraoperative transfusion as independent predictors of POAF5–9,11. The mechanisms of onset of POAF are specific and complex, including reduced cardiopulmonary reserve, altered sympathovagal balance, surgical damage to autonomic hilar nerve fibers, increased systemic inflammation, and peroxide stress responses12–14.
Although several treatment methods14–16 for new clinically important POAF, mainly based on prophylactic therapy such as β-blocking agents or amiodarone, have achieved certain efficacy17,18, their clinical application has not been widespread because of potential complications. In addition, although some models from Europe and the United States have been developed to predict new clinically important POAF after thoracic surgery19, their application in Asian populations remains unclear owing to racial differences. Moreover, prior studies had limitations such as poor model performance with the best area under the receiver operating characteristic curve (AUROC) of 0.720, unavailability of data (such as BNP), small numbers of patients analyzed, and lack of external validation7–9,19. Therefore, a well-performing model developed based on available factors is needed to predict the occurrence of new clinically important POAF after thoracoscopic anatomical lung cancer surgery, which may facilitate early intervention for high-risk patients and optimize the management of this event.
Materials and methods
Study design and patients
This retrospective study was performed after the approval of the Institutional Review Board of Shanghai Chest Hospital (IS21119), with informed consent waived. This study included 15 419 consecutive patients who underwent thoracoscopic anatomical lung cancer surgery from January 2016 to December 2018 in Shanghai Chest Hospital. Patients who had preoperative AF or nonsinus rhythm, pneumonectomy or sleeve lobectomy or bilateral lung resection, and missing data for any variables were excluded. Of included 14 074 patients in the final analysis, we selected 8717 participants for the training cohort and 5357 participants for the testing cohort, based on the split date of 1 January 2018. In terms of external validation, following the approval of the Institutional Review Board of Shanghai Ruijin Hospital (2022-209), and after meeting the same inclusion and exclusion criteria described, we additionally pooled 2941 consecutive patients who underwent this surgical treatment from July 2016 to July 2021 in Shanghai Ruijin Hospital. The study flowchart was described in Figure 1. This article adheres to strengthening the reporting of cohort, cross-sectional and case–control studies in surgery (STROCSS) guidelines20 (Supplemental Digital Content 1, http://links.lww.com/JS9/B618).
Figure 1.
Study flowchart.
Data collection and definition
Perioperative clinical data were prospectively collected from the electronic medical record of two institutions. The following variables were included: age, sex, BMI, American Society of Anesthesiologists (ASA) physical status classification, preoperative comorbidity, preoperative treatment (including chemoradiotherapy chemotherapy, radiotherapy, and immunotherapy), tumor size, clinical T stage, lymph nodes calcification, clinical nodal involvement, pleural adhesions, type of resection (segmentectomy and lobectomy), approach (video-assisted thoracoscopic surgery and robotic-assisted thoracoscopic surgery), location of resection (right and left), conversion to thoracotomy, operative time, and intraoperative arrhythmia and transfusion (shown in Table 1 for details). The tumor staging is based on the 8th edition of the TNM classification for lung cancer. Intraoperative arrhythmia mainly included the new-onset of AF, atrial flutter, and atrial premature beats, lasting at least 1 min, as recorded from the ECG21. According to the 2014 Guidelines of the American Association of Thoracic Surgeons (AATS)22, new clinically important POAF was defined as a new-onset of AF that resulted in symptoms or required treatment during postoperative hospitalization.
Table 1.
Baseline and intraoperative characteristics
| Derivation cohort | External | ||
|---|---|---|---|
| Variablesa | Training cohort (n=8,717) | Testing cohort (n=5,357) | Validation cohort (n=2,941) |
| Age, year | 59.0 (51.0-65.0) | 60.0 (52.0-66.0) | 59.0 (51.0-66.0) |
| Sex | |||
| Male sex | 5252 (60.3) | 3186 (59.5) | 1207 (41.0) |
| Female sex | 3465 (39.7) | 2171 (40.5) | 1734 (59.0) |
| BMI, kg/m2 | 23.1 (21.2-25.1) | 23.2 (21.3-25.1) | 23.2 (21.3-25.1) |
| ASA grade | |||
| I | 816 (9.4) | 400 (7.5) | 64 (2.2) |
| II | 7021 (80.5) | 4426 (82.6) | 2470 (84.0) |
| III/IV | 880 (10.1) | 531 (9.9) | 407 (13.8) |
| Comorbidity | |||
| Hypertension | 578 (6.6) | 470 (8.8) | 903 (30.7) |
| Diabetes mellitus | 282 (3.2) | 225 (4.2) | 242 (8.2) |
| CAD | 38 (0.4) | 58 (1.1) | 78 (2.7) |
| Stroke/TIA | 17 (0.2) | 30 (0.6) | 62 (2.1) |
| Preoperative treatment | 31 (0.4) | 13 (0.2) | 26 (0.9) |
| Tumor size, cm | 1.5 (1.0-2.2) | 1.5 (1.0-2.2) | 1.4 (1.0-2.1) |
| Clinical T stage (T≥2) | 924 (10.6) | 519 (9.7) | 270 (9.2) |
| Lymph nodes calcification | 408 (4.7) | 297 (5.5) | 151 (5.1) |
| Clinical nodal involvement | 439 (5.0) | 256 (4.8) | 225 (7.7) |
| Pleural adhesions | 201 (2.3) | 105 (2.0) | 95 (3.2) |
| Type of resection | |||
| Segmentectomy | 1798 (20.6) | 1307 (24.4) | 846 (28.8) |
| Lobectomy | 6919 (79.4) | 4050 (75.6) | 2095 (71.2) |
| Approach | |||
| VATS | 8326 (95.5) | 5157 (96.3) | 2374 (80.7) |
| RATS | 391 (4.5) | 200 (3.7) | 567 (19.3) |
| Location of resection | |||
| Left | 3350 (38.4) | 2093 (39.1) | 1109 (37.7) |
| Right | 5367 (61.6) | 3264 (60.9) | 1832 (62.3) |
| Conversion to thoracotomy | 122 (1.4) | 52 (1.0) | 71 (2.4) |
| Operative time, min | 91.0 (73.0-115.0) | 91.0 (71.0-114.0) | 120.0 (95.0-150.0) |
| Intraoperative arrhythmia | 149 (1.7) | 71 (1.3) | 14 (0.5) |
| Intraoperative transfusion | 89 (1.0) | 44 (0.8) | 33 (1.1) |
Continuous data was shown as median (interquartile range, Q1-Q3) and categoric data as number (%).
ASA, American Society of Anesthesiology; BMI, Body mass index; CAD, Coronary artery disease; RATS, Robotic-assisted thoracoscopic surgery; TIA, Transient cerebral ischemic attack; VATS, Video-assisted thoracoscopic surgery.
Preoperative treatment includes chemotherapy, radiotherapy, chemoradiotherapy, and immunotherapy.
Statistical analysis
Candidate predictors selection
Continuous variables were compared according to the occurrence of new clinically important POAF using two-independent-sample t-test or the Mann–Whitney U test. Categorical variables were compared using the χ2 test or Fisher’s exact test, depending on the sample size. All factors significantly associated with new clinically important POAF in the univariate analysis (P<0.2) were entered into the multivariate logistic regression model using the forward selection strategy.
Model development and evaluation
The prediction model was presented with a nomogram to provide a visual point system for estimating the probability of a new-onset clinically important POAF. Discrimination (concordance statistic (C-statistic) complying with the performance of AUROC was used to assess the discriminative ability of the model) and calibration (depicted by calibration curves and plot, evaluated by H-L goodness-of-fit test value or Brier score; a large P-value (>0.05) of H-L test or Brier score <0.25 both indicated good calibration) were applied to evaluate the model performance. To lessen overfitting and quantify optimism, the model was internally validated with an approach to 1000 bootstrapped resampling and calculating an optimism-corrected C-statistic. The clinical validity and net benefit were evaluated by decision curve analysis (DCA), and larger threshold risk range indicated wider clinical application23.
SPSS 26.0 software (IBM Corp) was used for statistical analysis. R version 4.1.2 was performed with rms, forestplot, tidyr, dplyr, PredictABEL, pROC, rmda, and ResourceSelection packages. P-value <0.05 was considered statistically significant.
Results
Model development
In the training cohort, 2.6% (230 out of 8,717) patients occurred new clinically important POAF in the training cohort. Multivariate analysis showed that age [odds ratio (OR)=1.070, 95% CI: 1.054–1.087, P<0.001], hypertension (OR=1.703, 95% CI: 1.150–2.522, P=0.008), preoperative treatment (OR=4.264, 95% CI: 1.243–14.624, P=0.021), clinical T stage ≥2 (OR=1.436, 95% CI: 1.023–2.014, P=0.036), intraoperative arrhythmia (OR=2.955, 95% CI: 1.684–5.186, P<0.001) and transfusion (OR=3.395, 95% CI: 1.793–6.429, P<0.001), and operative time (OR=1.009, 95% CI: 1.006–1.012, P<0.001) were independent predictors of new clinically important POAF (Fig. 2A). These seven candidate predictors were used to develop a nomogram for predicting the probability of new clinically important POAF (Fig. 2B).
Figure 2.
A forest plot of independent predictors of new clinically important postoperative atrial fibrillation (POAF). B Nomogram of the established model. The nomogram provides a visual point system based on the combination of patient characteristics (age, hypertension, preoperative treatment, clinical T stage, intraoperative arrhythmia and transfusion, and operative time) to estimate the probability of clinically POAF. To calculate the probability of clinically POAF, the points of seven variables determined on the scale were added to obtain the total points. Draw a vertical line from the total points scale to the last axis to obtain the corresponding probability of new important clinically POAF.
Model performance and internal validation
The C-statistic value of the developed model was 0.740 (95% CI: 0.709–0.771), and its sensitivity and specificity were 82.6 and 51.4%, respectively, which showed good discrimination (Fig. 3A). The H-L goodness-of-fit value was 0.158 and the Brier score was 0.025, indicating good calibration. To reduce the overfitting and optimism of the developed model, internal validation with 1000 bootstrap approach was conducted, which reflected good discrimination with optimism-corrected C-statistic of 0.736 (95% CI: 0.705–0.768). The bias-corrected calibration curve revealed that the prediction model was well calibrated when the actual observed probability of POAF was less than 15%, (Fig. 3B). The DCA of the established model indicated positive net benefits with the threshold risk range of 0–100% (Fig. 3C).
Figure 3.
A discrimination of the established model in the training cohort. The area under the receiver operating characteristic curve was calculated to evaluate the discrimination of the established model in the training cohort. B Calibration curves of the established model in the training cohort. An accurate prediction model will generate a plot where the actual observed probability corresponds exactly to the predicted probability of postoperative atrial fibrillation (POAF) and falls along the 45° line (dashed line). The apparent calibration curve (dotted line) represents the calibration of the model in the training cohort, while the bias-corrected curve (solid line) is the calibration result after adjusting for optimism through 1000 bootstrap-resampling. C DCA of the established model in the training cohort. The Y-axis represents net benefit. The solid red line is a nomogram predicting the risk of POAF. The solid gray line indicates that all patients developed POAF, while the fine solid black line indicates that no patient developed POAF. In this analysis, the decision curve provides a larger net benefit, with ranges of 0 and 100%. DCA, decision curve analysis.
Testing and external validation
In the testing and external validation cohorts, 2.9% (158 out of 5,357) and 1.0% (29 out of 2,941) patients occurred new clinically important POAF. The established model showed good discrimination in estimating the risk of new clinically important POAF, with a C-statistic value of 0.717 in the testing cohort and 0.768 in the external validation cohort (Fig. 4A–B). The sensitivity and specificity of AUROC was 64.3 and 70.9% in the testing cohort, and 80.6 and 69.0% in the external validation cohort. Calibration plots for the testing and external validation were depicted in Supplementary Figure 1 (Supplemental Digital Content 2, http://links.lww.com/JS9/B619). And the Brier score was 0.028 and 0.012 in the testing and external validation cohorts, both indicating good calibration of the model.
Figure 4.
A discrimination of the established model in the testing cohort. B Discrimination of the established model in the external validation cohort. The area under the receiver operating characteristic curve was calculated to evaluate the discrimination of the established model in the testing (4A) and external validation cohort (4B).
Discussion
In thoracoscopic anatomical lung cancer surgery, this study identified seven independent predictors of new clinically important POAF, among which preoperative treatment, intraoperative arrhythmia, and operative time were rarely reported. By using these seven candidate predictors, this study developed and externally validated a novel model for estimating the risk of new clinically important POAF, with good performance and a wide range of positive net benefit.
Previous studies have identified some underlying predictors of new clinically important POAF5–9,11,18,20. This study also verified that older age, hypertension, advanced T stage, and intraoperative transfusion were independent predictors of new clinically important POAF. Reduced pulmonary function reserve, increased comorbidities, and tissue fragility24, and age-related myocardial apoptosis and fibrosis12 in elderly patients may be associated with cardiac remodeling, delayed atrial conduction, and reentry of circuits, leading to the occurrence of new clinically important POAF. The susceptibility of patients with hypertension to new clinically important POAF may be related to their larger atrial volume25 and increased cardiovascular comorbidities such as congestive heart failure5,26.
Moreover, the combination of the predictive ability of clinical T stage (T ≥2) and the lack of predictive ability of the type of resection indicated that the extent of hilar dissection (rather than the extent of parenchymal resection) may be a causal factor for the development of new clinically important POAF, which was consistent with the purported origin of paroxysmal AF in the pulmonary veins27. Our study also found an association between intraoperative transfusion and new clinically important POAF, which has not been widely reported in the literature5,7. Although the immune effects of transfusion may lead to an increase in arrhythmias, transfusion is more likely to be a more complex or difficult anatomical marker. Thus, reducing intraoperative transfusion and giving prophylactic therapy such as β-blocking agents or amiodarone to patients at high-risk of transfusion may help reduce the occurrence of new clinically important POAF.
Meanwhile, the current study also identified other potential predictors rarely reported for new clinically important POAF, including preoperative treatment, intraoperative new-onset arrhythmia, and operative time. Usually, patients treated with preoperative treatment have a high clinical T stage. In these patients, the tumor may directly block the regional anatomical area near the vagus nerve and its branches27, increasing the difficulty of endoscopic resection28,29 and the possibility of the subsequent occurrence of new clinical important POAF.
The relationship between a history of arrhythmia and POAF has been clearly stated in previous reports5–7,9. However, the relationship between intraoperative new-onset arrhythmia, including AF and atrial flutter, and new clinically important POAF remains unclear. The study by Lanters et al.30 showed that intraoperative AF inducibility did not predict the development of POAF, whereas this study suggested that intraoperative new-onset arrhythmia was an independent predictor of new clinically important POAF. The discrepancy between the two outcomes may be due to differences in the type of procedure and sample size analyzed. The association between prolonged operative time and the risk of new clinically important POAF has not been widely recognized by other researchers. However, it was confirmed in the current study and was possibly explained by increased systemic inflammation and surgical stress related to prolonged operative time12,13. Moreover, recent studies showed that operative time was an important predictor of postoperative adverse outcomes, suggesting that shortening operative time may be an important part of reducing the rate of new clinically important POAF31,32.
For model construction, all variables included in the prediction model were quantifiable predictors readily available to clinicians. Our established model can precisely predict the risk of new clinically important POAF, with the mean AUCs of 0.740, sensitivity of 0.826, which can be effective in identifying and diagnosing patients with or without such complication. In addition, the novel nomogram provides a visual point system for estimating the probability of new clinically important POAF and had well performance in the testing and external validation cohorts, outperforming established models without external validation by the studies of Onaitis (AUC=0.664) and Amar (AUC=0.720)7,9. The bias-corrected calibration curve showed that the model accurately predicted the risk of new clinically important POAF when the observed probability of POAF was less than 15%. Given that the incidence of new clinically important POAF after lung cancer surgery is mainly between 2.5 and 20%5–9, this prediction model may have good clinical significance. Brier score was 0.028 and 0.012 in the testing and external validation cohorts, indicating good calibration and accuracy of the model. The depicted DCA demonstrated that, within the threshold risk range of 0–100%, intervention decisions based on the prediction model were clearly beneficial.
This study had several limitations. First, as a retrospective study based on prospectively collected data, this study had inherent design biases. Second, laboratory data (such as BNP9) beyond clinical variables may be required to further improve the performance of the prediction model. Third, although the established model showed good efficacy in both internal and external validation cohorts, external validation in other populations outside of China was still needed.
Conclusions
The proposed novel model can be a useful and effective tool to predict the risk of new clinically important POAF following thoracoscopic anatomical lung cancer surgery. Its utilization may promote the application of prevention and treatment in high-risk patients, and reduce the development and related adverse outcomes of this event.
Ethical approval
This study was approved by the Institutional Review Board at Shanghai Chest Hospital (IS21119) and Shanghai Ruijin Hospital (2022-209), and the informed consent was waived because of the retrospective nature of the study.
Sources of funding
This work was supported by National Natural Science Foundation of China (82071233) and Shanghai Shen Kang Hospital Development Center Project (SHDC2020CR4063).
Author contribution
J.W., H.L., J.Z., and R.J.: conception and design; C.T., Z.N., H.Z., T.L., Y.X., Y.Y., and Q.M.: data collection; C.T., Z.N., and H.Z.: statistical analysis and data interpretation; C.T., Z.N., and H.: manuscript preparation; J.W., H.L., J.Z., and R.J.: critical revision.
Conflicts of interest disclosure
All authors have no conflicts of interest to declare.
Research registration unique identifying number (UIN)
Name of the registry: Development and external validation of a novel model for predicting new clinically important atrial fibrillation after thoracoscopic anatomical lung cancer surgery- A multi-center retrospective cohort study.
Unique identifying number or registration ID: researchregistry9893.
Hyperlink to your specific registration (must be publicly accessible and will be checked): https://researchregistry.knack.com/research-registry#home/registrationdetails/659cb62b26afff002842ae57/.
Guarantor
Jingxiang Wu (Corresponding author).
Data availability statement
Our research team could provide original data under reasonable request and with permission from Shanghai Chest Hospital and Shanghai Ruijin Hospital. (wjx1132xk@163.com).
Provenance and peer review
Not commissioned, externally peer-reviewed.
Supplementary Material
Footnotes
Chaoyang Tong, Zhenyi Niu, and Hongwei Zhu contributed equally to this work and should be considered co-first author.
Runsen Jin, Jijian Zheng, Hecheng Li, and Jingxiang Wu contributed equally to this work and should be considered co- corresponding author.
Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
Supplemental Digital Content is available for this article. Direct URL citations are provided in the HTML and PDF versions of this article on the journal's website, www.lww.com/international-journal-of-surgery.
Published online 4 January 2024
Contributor Information
Chaoyang Tong, Email: 2415007153@qq.com.
Zhenyi Niu, Email: zhenyiniu@qq.com.
Hongwei Zhu, Email: zhuhongwei06@163.com.
Tingting Li, Email: Ittdottie@126.com.
Yuanyuan Xu, Email: xyyvincent@foxmail.com.
Yan Yan, Email: yanyan990220@126.com.
Qing Miao, Email: miaoqmz@163.com.
Runsen Jin, Email: nkvincent@163.com.
Jijian Zheng, Email: zhengjijian626@sina.com.
Hecheng Li, Email: lihecheng2000@hotmail.com.
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Supplementary Materials
Data Availability Statement
Our research team could provide original data under reasonable request and with permission from Shanghai Chest Hospital and Shanghai Ruijin Hospital. (wjx1132xk@163.com).